Soyeon Jun
Impact in
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- Neural dynamics and brain function
- EEG and Brain-Computer Interfaces
- Functional Brain Connectivity Studies
- Neural and Behavioral Psychology Studies
- Memory and Neural Mechanisms
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- Neurological disorders and treatments
- Transcranial Magnetic Stimulation Studies
Papers in
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- Neural dynamics and brain function 4
- EEG and Brain-Computer Interfaces 3
- Memory and Neural Mechanisms 2
- Functional Brain Connectivity Studies 1
- Neural and Behavioral Psychology Studies 1
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- Neuroscience and Neuropharmacology Research 2
- Co-authors
- Chun Kee Chung (3 shared papers)June Sic Kim (3 shared papers)Woorim Jeong (1 shared paper)Sang Ah Lee (1 shared paper)James Young (1 shared paper)Hee‐Seok Oh (1 shared paper)Lara Marcuse (1 shared paper)Christina M. Maher (1 shared paper)
- Journals
- Frontiers in Human Neuroscience (1 paper)Proceedings of the National Academy of Sciences (1 paper)Brain stimulation (1 paper)IEEE Transactions on Biomedical Engineering (1 paper)Frontiers in Neuroscience (1 paper)
- Partner nations
- South KoreaUnited StatesEthiopia
In The Last Decade
Soyeon Jun
4 papers receiving 40 citations
Peers
Comparison fields: 5 of 14
- Cognitive Neuroscience 27
- Neurology 11
- Cellular and Molecular Neuroscience 13
- Neurology 5
- Developmental Neuroscience 1
Countries citing papers authored by Soyeon Jun
This map shows the geographic impact of Soyeon Jun's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Soyeon Jun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soyeon Jun more than expected).
Fields of papers citing papers by Soyeon Jun
This network shows the impact of papers produced by Soyeon Jun. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Soyeon Jun. The network helps show where Soyeon Jun may publish in the future.
Co-authors
The 15 scholars most cited alongside Soyeon Jun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 19 | |
| 2 | 2019 | 18 | |
| 3 | 2021 | 2 | |
| 4 | 2022 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2009 | 0 |
About Soyeon Jun
Soyeon Jun is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience, Automotive Engineering, Clinical Psychology and Electrical and Electronic Engineering, having authored 6 papers that have together received 40 indexed citations. Recurring topics across this work include Neural dynamics and brain function (4 papers), EEG and Brain-Computer Interfaces (3 papers), Memory and Neural Mechanisms (2 papers), Neuroscience and Neuropharmacology Research (2 papers), Energy Harvesting in Wireless Networks (1 paper), Advanced Battery Technologies Research (1 paper), Functional Brain Connectivity Studies (1 paper) and Neural and Behavioral Psychology Studies (1 paper). The work is most often cited by research in Cognitive Neuroscience (27 citations), Neurology (11 citations), Cellular and Molecular Neuroscience (13 citations), Neurology (5 citations) and Developmental Neuroscience (1 citation). Soyeon Jun has collaborated with scholars based in South Korea, United States and Ethiopia. Frequent co-authors include Chun Kee Chung, June Sic Kim, Woorim Jeong, Sang Ah Lee, James Young, Hee‐Seok Oh, Lara Marcuse, Christina M. Maher, Fedor Panov and Blake S. Wilson. Their work appears in journals such as Frontiers in Human Neuroscience, Proceedings of the National Academy of Sciences, Brain stimulation, IEEE Transactions on Biomedical Engineering and Frontiers in Neuroscience.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.